如何 return 'untidy' 多个标准统计数据的数据帧摘要，按行排列，用于 R 中数据帧的每一列？
How to return an 'untidy' dataframe summary of multiple standard statistics arranged by row for each column of a dataframe in R?
Pandas 在 Python 中有 describe() 函数 returns 数据帧的汇总统计信息。输出不是 'tidy' 格式以便使用 tidyverse summarize 函数进行简单操作，但它是一种很好的演示格式。我的问题是如何在 R 中重现此输出？
import pandas as pd
mtcars_df = pd.read_csv(filepath_or_buffer="data/mtcars.csv")
mtcars_df.describe()
'''
mpg cyl disp ... am gear carb
count 32.000000 32.000000 32.000000 ... 32.000000 32.000000 32.0000
mean 20.090625 6.187500 230.721875 ... 0.406250 3.687500 2.8125
std 6.026948 1.785922 123.938694 ... 0.498991 0.737804 1.6152
min 10.400000 4.000000 71.100000 ... 0.000000 3.000000 1.0000
25% 15.425000 4.000000 120.825000 ... 0.000000 3.000000 2.0000
50% 19.200000 6.000000 196.300000 ... 0.000000 4.000000 2.0000
75% 22.800000 8.000000 326.000000 ... 1.000000 4.000000 4.0000
max 33.900000 8.000000 472.000000 ... 1.000000 5.000000 8.0000
'''
为了在 R 中重现此输出，我使用了基本 R 摘要函数。不幸的是，输出重复了每一列上的统计标签。为了删除标签，我将 table 整理成一个数据框，并用正则表达式删除了标签！比我预期的要多得多的努力。如果 R 中有更简洁、更简单的方法，我很想知道。
library(tidyverse)
library(rebus)
#>
#> Attaching package: 'rebus'
#> The following object is masked from 'package:stringr':
#>
#> regex
#> The following object is masked from 'package:ggplot2':
#>
#> alpha
stats_table <- summary(mtcars)
stats_table
#> mpg cyl disp hp
#> Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
#> 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
#> Median :19.20 Median :6.000 Median :196.3 Median :123.0
#> Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
#> 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
#> Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
#> drat wt qsec vs
#> Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
#> 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
#> Median :3.695 Median :3.325 Median :17.71 Median :0.0000
#> Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
#> 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
#> Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
#> am gear carb
#> Min. :0.0000 Min. :3.000 Min. :1.000
#> 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
#> Median :0.0000 Median :4.000 Median :2.000
#> Mean :0.4062 Mean :3.688 Mean :2.812
#> 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
#> Max. :1.0000 Max. :5.000 Max. :8.000
pattern <- one_or_more(DGT) %R% optional(".") %R% optional(one_or_more(DGT))
get_labels <- as.data.frame.matrix(stats_table)[,1]
location <- str_locate_all(pattern =':', get_labels)[[1]][1]
strip_punct <- zero_or_more(PUNCT) %R% zero_or_more(SPACE) %R% PUNCT
identity <- str_remove_all(str_sub(string = get_labels, start = 1, end = location), strip_punct)
stats_df <- as.data.frame.matrix(stats_table) %>%
mutate(across(everything(), ~str_match(., pattern))) %>%
mutate(identity = identity) %>%
relocate(identity)
stats_df
#> identity mpg cyl disp hp drat wt qsec
#> X Min 10.4 4.0 71.1 52.0 2.7 1.5 14.5
#> X.1 1st Qu 1 1 1 1 1 1 1
#> X.2 Median 19.2 6.0 196.3 123.0 3.6 3.3 17.7
#> X.3 Mean 20.0 6.1 230.7 146.7 3.5 3.2 17.8
#> X.4 3rd Qu 3 3 3 3 3 3 3
#> X.5 Max 33.9 8.0 472.0 335.0 4.9 5.4 22.9
#> vs am gear carb
#> X 0.0 0.0 3.0 1.0
#> X.1 1 1 1 1
#> X.2 0.0 0.0 4.0 2.0
#> X.3 0.4 0.4 3.6 2.8
#> X.4 3 3 3 3
#> X.5 1.0 1.0 5.0 8.0
我可以使用 tidyverse 和 summarize 函数生成相同的值，但所有内容都在一行中，而不是按行汇总每列的统计信息。这使得阅读和呈现变得相当困难。
mtcars %>%
summarise_all( .funs = list(
min = min,
mean = ~ mean(., na.rm=TRUE),
median = median,
stdev = sd,
percentile_25 = ~ quantile(., .25)[[1]],
percentile_75 = ~ quantile(., .75)[[1]],
max = max)
) %>% glimpse()
#> Rows: 1
#> Columns: 77
#> $ mpg_min <dbl> 10.4
#> $ cyl_min <dbl> 4
#> $ disp_min <dbl> 71.1
#> $ hp_min <dbl> 52
#> $ drat_min <dbl> 2.76
#> $ wt_min <dbl> 1.513
#> $ qsec_min <dbl> 14.5
#> $ vs_min <dbl> 0
#> $ am_min <dbl> 0
#> $ gear_min <dbl> 3
#> $ carb_min <dbl> 1
#> $ mpg_mean <dbl> 20.09062
#> $ cyl_mean <dbl> 6.1875
#> $ disp_mean <dbl> 230.7219
#> $ hp_mean <dbl> 146.6875
#> $ drat_mean <dbl> 3.596563
#> $ wt_mean <dbl> 3.21725
#> $ qsec_mean <dbl> 17.84875
#> $ vs_mean <dbl> 0.4375
#> $ am_mean <dbl> 0.40625
#> $ gear_mean <dbl> 3.6875
#> $ carb_mean <dbl> 2.8125
#> $ mpg_median <dbl> 19.2
#> $ cyl_median <dbl> 6
#> $ disp_median <dbl> 196.3
#> $ hp_median <dbl> 123
#> $ drat_median <dbl> 3.695
#> $ wt_median <dbl> 3.325
#> $ qsec_median <dbl> 17.71
#> $ vs_median <dbl> 0
#> $ am_median <dbl> 0
#> $ gear_median <dbl> 4
#> $ carb_median <dbl> 2
#> $ mpg_stdev <dbl> 6.026948
#> $ cyl_stdev <dbl> 1.785922
#> $ disp_stdev <dbl> 123.9387
#> $ hp_stdev <dbl> 68.56287
#> $ drat_stdev <dbl> 0.5346787
#> $ wt_stdev <dbl> 0.9784574
#> $ qsec_stdev <dbl> 1.786943
#> $ vs_stdev <dbl> 0.5040161
#> $ am_stdev <dbl> 0.4989909
#> $ gear_stdev <dbl> 0.7378041
#> $ carb_stdev <dbl> 1.6152
#> $ mpg_percentile_25 <dbl> 15.425
#> $ cyl_percentile_25 <dbl> 4
#> $ disp_percentile_25 <dbl> 120.825
#> $ hp_percentile_25 <dbl> 96.5
#> $ drat_percentile_25 <dbl> 3.08
#> $ wt_percentile_25 <dbl> 2.58125
#> $ qsec_percentile_25 <dbl> 16.8925
#> $ vs_percentile_25 <dbl> 0
#> $ am_percentile_25 <dbl> 0
#> $ gear_percentile_25 <dbl> 3
#> $ carb_percentile_25 <dbl> 2
#> $ mpg_percentile_75 <dbl> 22.8
#> $ cyl_percentile_75 <dbl> 8
#> $ disp_percentile_75 <dbl> 326
#> $ hp_percentile_75 <dbl> 180
#> $ drat_percentile_75 <dbl> 3.92
#> $ wt_percentile_75 <dbl> 3.61
#> $ qsec_percentile_75 <dbl> 18.9
#> $ vs_percentile_75 <dbl> 1
#> $ am_percentile_75 <dbl> 1
#> $ gear_percentile_75 <dbl> 4
#> $ carb_percentile_75 <dbl> 4
#> $ mpg_max <dbl> 33.9
#> $ cyl_max <dbl> 8
#> $ disp_max <dbl> 472
#> $ hp_max <dbl> 335
#> $ drat_max <dbl> 4.93
#> $ wt_max <dbl> 5.424
#> $ qsec_max <dbl> 22.9
#> $ vs_max <dbl> 1
#> $ am_max <dbl> 1
#> $ gear_max <dbl> 5
#> $ carb_max <dbl> 8
由 reprex package (v2.0.1)
创建于 2022-03-12
您可以将 do.call() 与 rind() 和 lapply() 结合使用以获得 summary() 的整洁格式。 t() 转置输出。
t(do.call(rbind, lapply(mtcars, summary)))
#> mpg cyl disp hp drat wt qsec vs
#> Min. 10.40000 4.0000 71.1000 52.0000 2.760000 1.51300 14.50000 0.0000
#> 1st Qu. 15.42500 4.0000 120.8250 96.5000 3.080000 2.58125 16.89250 0.0000
#> Median 19.20000 6.0000 196.3000 123.0000 3.695000 3.32500 17.71000 0.0000
#> Mean 20.09062 6.1875 230.7219 146.6875 3.596563 3.21725 17.84875 0.4375
#> 3rd Qu. 22.80000 8.0000 326.0000 180.0000 3.920000 3.61000 18.90000 1.0000
#> Max. 33.90000 8.0000 472.0000 335.0000 4.930000 5.42400 22.90000 1.0000
#> am gear carb
#> Min. 0.00000 3.0000 1.0000
#> 1st Qu. 0.00000 3.0000 2.0000
#> Median 0.00000 4.0000 2.0000
#> Mean 0.40625 3.6875 2.8125
#> 3rd Qu. 1.00000 4.0000 4.0000
#> Max. 1.00000 5.0000 8.0000
由 reprex package (v2.0.1)
创建于 2022-03-12
另一个考虑因素可能是 psych 包及其 describe 函数。
t(psych::describe(mtcars))
马特！您也可以尝试 dlookr::describe(mtcars)。输出是一个 tibble (tbl_df) https://choonghyunryu.github.io/dlookr/reference/describe.data.frame.html
另一个让我想起的包是 skimr 包中的 skim() 函数。
library(skimr)
library(tidyverse)
mtcars %>% skim()
Name
Piped data
Number of rows
32
Number of columns
11
_______________________
Column type frequency:
numeric
11
________________________
Group variables
None
数据汇总
变量类型：数值
skim_variable
n_missing
complete_rate
mean
sd
p0
p25
p50
p75
p100
hist
mpg
0
1
20.09
6.03
10.40
15.43
19.20
22.80
33.90
▃▇▅▁▂
cyl
0
1
6.19
1.79
4.00
4.00
6.00
8.00
8.00
▆▁▃▁▇
disp
0
1
230.72
123.94
71.10
120.83
196.30
326.00
472.00
▇▃▃▃▂
hp
0
1
146.69
68.56
52.00
96.50
123.00
180.00
335.00
▇▇▆▃▁
drat
0
1
3.60
0.53
2.76
3.08
3.70
3.92
4.93
▇▃▇▅▁
wt
0
1
3.22
0.98
1.51
2.58
3.33
3.61
5.42
▃▃▇▁▂
qsec
0
1
17.85
1.79
14.50
16.89
17.71
18.90
22.90
▃▇▇▂▁
vs
0
1
0.44
0.50
0.00
0.00
0.00
1.00
1.00
▇▁▁▁▆
am
0
1
0.41
0.50
0.00
0.00
0.00
1.00
1.00
▇▁▁▁▆
gear
0
1
3.69
0.74
3.00
3.00
4.00
4.00
5.00
▇▁▆▁▂
carb
0
1
2.81
1.62
1.00
2.00
2.00
4.00
8.00
▇▂▅▁▁
由 reprex package (v2.0.1)
创建于 2022-03-13
Pandas 在 Python 中有 describe() 函数 returns 数据帧的汇总统计信息。输出不是 'tidy' 格式以便使用 tidyverse summarize 函数进行简单操作，但它是一种很好的演示格式。我的问题是如何在 R 中重现此输出？
import pandas as pd
mtcars_df = pd.read_csv(filepath_or_buffer="data/mtcars.csv")
mtcars_df.describe()
'''
mpg cyl disp ... am gear carb
count 32.000000 32.000000 32.000000 ... 32.000000 32.000000 32.0000
mean 20.090625 6.187500 230.721875 ... 0.406250 3.687500 2.8125
std 6.026948 1.785922 123.938694 ... 0.498991 0.737804 1.6152
min 10.400000 4.000000 71.100000 ... 0.000000 3.000000 1.0000
25% 15.425000 4.000000 120.825000 ... 0.000000 3.000000 2.0000
50% 19.200000 6.000000 196.300000 ... 0.000000 4.000000 2.0000
75% 22.800000 8.000000 326.000000 ... 1.000000 4.000000 4.0000
max 33.900000 8.000000 472.000000 ... 1.000000 5.000000 8.0000
'''
为了在 R 中重现此输出，我使用了基本 R 摘要函数。不幸的是，输出重复了每一列上的统计标签。为了删除标签，我将 table 整理成一个数据框，并用正则表达式删除了标签！比我预期的要多得多的努力。如果 R 中有更简洁、更简单的方法，我很想知道。
library(tidyverse)
library(rebus)
#>
#> Attaching package: 'rebus'
#> The following object is masked from 'package:stringr':
#>
#> regex
#> The following object is masked from 'package:ggplot2':
#>
#> alpha
stats_table <- summary(mtcars)
stats_table
#> mpg cyl disp hp
#> Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
#> 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
#> Median :19.20 Median :6.000 Median :196.3 Median :123.0
#> Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
#> 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
#> Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
#> drat wt qsec vs
#> Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
#> 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
#> Median :3.695 Median :3.325 Median :17.71 Median :0.0000
#> Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
#> 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
#> Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
#> am gear carb
#> Min. :0.0000 Min. :3.000 Min. :1.000
#> 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
#> Median :0.0000 Median :4.000 Median :2.000
#> Mean :0.4062 Mean :3.688 Mean :2.812
#> 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
#> Max. :1.0000 Max. :5.000 Max. :8.000
pattern <- one_or_more(DGT) %R% optional(".") %R% optional(one_or_more(DGT))
get_labels <- as.data.frame.matrix(stats_table)[,1]
location <- str_locate_all(pattern =':', get_labels)[[1]][1]
strip_punct <- zero_or_more(PUNCT) %R% zero_or_more(SPACE) %R% PUNCT
identity <- str_remove_all(str_sub(string = get_labels, start = 1, end = location), strip_punct)
stats_df <- as.data.frame.matrix(stats_table) %>%
mutate(across(everything(), ~str_match(., pattern))) %>%
mutate(identity = identity) %>%
relocate(identity)
stats_df
#> identity mpg cyl disp hp drat wt qsec
#> X Min 10.4 4.0 71.1 52.0 2.7 1.5 14.5
#> X.1 1st Qu 1 1 1 1 1 1 1
#> X.2 Median 19.2 6.0 196.3 123.0 3.6 3.3 17.7
#> X.3 Mean 20.0 6.1 230.7 146.7 3.5 3.2 17.8
#> X.4 3rd Qu 3 3 3 3 3 3 3
#> X.5 Max 33.9 8.0 472.0 335.0 4.9 5.4 22.9
#> vs am gear carb
#> X 0.0 0.0 3.0 1.0
#> X.1 1 1 1 1
#> X.2 0.0 0.0 4.0 2.0
#> X.3 0.4 0.4 3.6 2.8
#> X.4 3 3 3 3
#> X.5 1.0 1.0 5.0 8.0
我可以使用 tidyverse 和 summarize 函数生成相同的值，但所有内容都在一行中，而不是按行汇总每列的统计信息。这使得阅读和呈现变得相当困难。
mtcars %>%
summarise_all( .funs = list(
min = min,
mean = ~ mean(., na.rm=TRUE),
median = median,
stdev = sd,
percentile_25 = ~ quantile(., .25)[[1]],
percentile_75 = ~ quantile(., .75)[[1]],
max = max)
) %>% glimpse()
#> Rows: 1
#> Columns: 77
#> $ mpg_min <dbl> 10.4
#> $ cyl_min <dbl> 4
#> $ disp_min <dbl> 71.1
#> $ hp_min <dbl> 52
#> $ drat_min <dbl> 2.76
#> $ wt_min <dbl> 1.513
#> $ qsec_min <dbl> 14.5
#> $ vs_min <dbl> 0
#> $ am_min <dbl> 0
#> $ gear_min <dbl> 3
#> $ carb_min <dbl> 1
#> $ mpg_mean <dbl> 20.09062
#> $ cyl_mean <dbl> 6.1875
#> $ disp_mean <dbl> 230.7219
#> $ hp_mean <dbl> 146.6875
#> $ drat_mean <dbl> 3.596563
#> $ wt_mean <dbl> 3.21725
#> $ qsec_mean <dbl> 17.84875
#> $ vs_mean <dbl> 0.4375
#> $ am_mean <dbl> 0.40625
#> $ gear_mean <dbl> 3.6875
#> $ carb_mean <dbl> 2.8125
#> $ mpg_median <dbl> 19.2
#> $ cyl_median <dbl> 6
#> $ disp_median <dbl> 196.3
#> $ hp_median <dbl> 123
#> $ drat_median <dbl> 3.695
#> $ wt_median <dbl> 3.325
#> $ qsec_median <dbl> 17.71
#> $ vs_median <dbl> 0
#> $ am_median <dbl> 0
#> $ gear_median <dbl> 4
#> $ carb_median <dbl> 2
#> $ mpg_stdev <dbl> 6.026948
#> $ cyl_stdev <dbl> 1.785922
#> $ disp_stdev <dbl> 123.9387
#> $ hp_stdev <dbl> 68.56287
#> $ drat_stdev <dbl> 0.5346787
#> $ wt_stdev <dbl> 0.9784574
#> $ qsec_stdev <dbl> 1.786943
#> $ vs_stdev <dbl> 0.5040161
#> $ am_stdev <dbl> 0.4989909
#> $ gear_stdev <dbl> 0.7378041
#> $ carb_stdev <dbl> 1.6152
#> $ mpg_percentile_25 <dbl> 15.425
#> $ cyl_percentile_25 <dbl> 4
#> $ disp_percentile_25 <dbl> 120.825
#> $ hp_percentile_25 <dbl> 96.5
#> $ drat_percentile_25 <dbl> 3.08
#> $ wt_percentile_25 <dbl> 2.58125
#> $ qsec_percentile_25 <dbl> 16.8925
#> $ vs_percentile_25 <dbl> 0
#> $ am_percentile_25 <dbl> 0
#> $ gear_percentile_25 <dbl> 3
#> $ carb_percentile_25 <dbl> 2
#> $ mpg_percentile_75 <dbl> 22.8
#> $ cyl_percentile_75 <dbl> 8
#> $ disp_percentile_75 <dbl> 326
#> $ hp_percentile_75 <dbl> 180
#> $ drat_percentile_75 <dbl> 3.92
#> $ wt_percentile_75 <dbl> 3.61
#> $ qsec_percentile_75 <dbl> 18.9
#> $ vs_percentile_75 <dbl> 1
#> $ am_percentile_75 <dbl> 1
#> $ gear_percentile_75 <dbl> 4
#> $ carb_percentile_75 <dbl> 4
#> $ mpg_max <dbl> 33.9
#> $ cyl_max <dbl> 8
#> $ disp_max <dbl> 472
#> $ hp_max <dbl> 335
#> $ drat_max <dbl> 4.93
#> $ wt_max <dbl> 5.424
#> $ qsec_max <dbl> 22.9
#> $ vs_max <dbl> 1
#> $ am_max <dbl> 1
#> $ gear_max <dbl> 5
#> $ carb_max <dbl> 8
由 reprex package (v2.0.1)
创建于 2022-03-12您可以将 do.call() 与 rind() 和 lapply() 结合使用以获得 summary() 的整洁格式。 t() 转置输出。
t(do.call(rbind, lapply(mtcars, summary)))
#> mpg cyl disp hp drat wt qsec vs
#> Min. 10.40000 4.0000 71.1000 52.0000 2.760000 1.51300 14.50000 0.0000
#> 1st Qu. 15.42500 4.0000 120.8250 96.5000 3.080000 2.58125 16.89250 0.0000
#> Median 19.20000 6.0000 196.3000 123.0000 3.695000 3.32500 17.71000 0.0000
#> Mean 20.09062 6.1875 230.7219 146.6875 3.596563 3.21725 17.84875 0.4375
#> 3rd Qu. 22.80000 8.0000 326.0000 180.0000 3.920000 3.61000 18.90000 1.0000
#> Max. 33.90000 8.0000 472.0000 335.0000 4.930000 5.42400 22.90000 1.0000
#> am gear carb
#> Min. 0.00000 3.0000 1.0000
#> 1st Qu. 0.00000 3.0000 2.0000
#> Median 0.00000 4.0000 2.0000
#> Mean 0.40625 3.6875 2.8125
#> 3rd Qu. 1.00000 4.0000 4.0000
#> Max. 1.00000 5.0000 8.0000
由 reprex package (v2.0.1)
创建于 2022-03-12另一个考虑因素可能是 psych 包及其 describe 函数。
t(psych::describe(mtcars))
马特！您也可以尝试 dlookr::describe(mtcars)。输出是一个 tibble (tbl_df) https://choonghyunryu.github.io/dlookr/reference/describe.data.frame.html
另一个让我想起的包是 skimr 包中的 skim() 函数。
library(skimr)
library(tidyverse)
mtcars %>% skim()
| Name | Piped data |
| Number of rows | 32 |
| Number of columns | 11 |
| _______________________ | |
| Column type frequency: | |
| numeric | 11 |
| ________________________ | |
| Group variables | None |
数据汇总
变量类型：数值
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| mpg | 0 | 1 | 20.09 | 6.03 | 10.40 | 15.43 | 19.20 | 22.80 | 33.90 | ▃▇▅▁▂ |
| cyl | 0 | 1 | 6.19 | 1.79 | 4.00 | 4.00 | 6.00 | 8.00 | 8.00 | ▆▁▃▁▇ |
| disp | 0 | 1 | 230.72 | 123.94 | 71.10 | 120.83 | 196.30 | 326.00 | 472.00 | ▇▃▃▃▂ |
| hp | 0 | 1 | 146.69 | 68.56 | 52.00 | 96.50 | 123.00 | 180.00 | 335.00 | ▇▇▆▃▁ |
| drat | 0 | 1 | 3.60 | 0.53 | 2.76 | 3.08 | 3.70 | 3.92 | 4.93 | ▇▃▇▅▁ |
| wt | 0 | 1 | 3.22 | 0.98 | 1.51 | 2.58 | 3.33 | 3.61 | 5.42 | ▃▃▇▁▂ |
| qsec | 0 | 1 | 17.85 | 1.79 | 14.50 | 16.89 | 17.71 | 18.90 | 22.90 | ▃▇▇▂▁ |
| vs | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| am | 0 | 1 | 0.41 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| gear | 0 | 1 | 3.69 | 0.74 | 3.00 | 3.00 | 4.00 | 4.00 | 5.00 | ▇▁▆▁▂ |
| carb | 0 | 1 | 2.81 | 1.62 | 1.00 | 2.00 | 2.00 | 4.00 | 8.00 | ▇▂▅▁▁ |
由 reprex package (v2.0.1)
创建于 2022-03-13